Manufacturers have long benefited from reusing returned products and parts. This benevolent approach can minimize cost and help the manufacturer to play a role in sustaining the environment, something which is of utmost importance these days because of growing environment concerns. Reuse of returned parts and products aids environment sustainability because doing so helps reduce the use of raw materials, eliminate energy use to produce new parts, and minimize waste materials. However, handling returns effectively and efficiently can be difficult if the processes do not provide the visibility that is necessary to track, manage, and re-use the returns. This paper applies advanced analytics on procurement data to increase reutilization in new build by optimizing Equal-to-New (ETN) parts return. This will reduce 'the spend' on new buy parts for building new product units. The process involves forecasting and matching returns supply to demand for new build. Complexity in the process is the forecasting and matching while making sure a reutilization engineering process is available. Also, this will identify high demand/value/yield parts for development engineering to focus. Analytics has been applied on different levels to enhance the optimization process including forecast of upgraded parts. Machine Learning algorithms are used to build an automated infrastructure that can support the transformation of ETN parts utilization in the procurement parts planning process. This system incorporate returns forecast in the planning cycle to reduce suppliers liability from 9 weeks to 12 months planning cycle, e.g., reduce 5% of 10 million US dollars liability.

本文应用高级分析技术在采购数据上进行优化，以提高等同于新机(EQN)零件的回收再利用率，从而减少新零件采购成本，并减少回收处理的能源和材料浪费。在此过程中，机器学习算法被用于构建自动化的基础架构，以支持采购零件规划过程和降低供应商的责任。

基于需求的资产再利用分析